.png){kind=icon}
.png){kind=icon}
Return
Exploiting Object Similarity for Robotic Visual Recognition
.png){kind=icon}
PRE
AI
DOI：10.1109/TRO.2020.3005531.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, we are interested in robotic visual object classification using a deep convolutional neural network (DCNN) classifier. We show that the correlation coefficient of the automatically learned DCNN features of two object images carries robust information on their similarity, and can be utilized to significantly improve the robot's classification accuracy, without additional training. More specifically, we first probabilistically analyze how the feature correlation carries vital similarity information and build a correlation-based Markov random field (CoMRF) for joint object labeling. Given query and motion budgets, we then propose an optimization framework to plan the robot's query and path based on our CoMRF. This gives the robot a new way to optimally decide which object sites to move close to for better sensing and for which objects to ask a remote human for help with classification, which considerably improves the overall classification. We extensively evaluate our proposed approach on two large datasets (e.g., drone imagery and indoor scenes) and several real-world robotic experiments. The results show that our proposed approach significantly outperforms the benchmarks.
Keywords:
Robot sensing systems
Visualization
Labeling
Correlation
Training
Measurement
Artificial intelligence (AI)-based methods
co-optimization of robotic path planning
deep learning in robotics and automation
object detection
querying
and visual recognition
segmentation
and categorization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
3.3K
Citations:
2.8W
